Model-based comparison of metabolic state of patients from different cohorts

OData support
Supervisor:
Homlok József
Department of Control Engineering and Information Technology

During and after liver-transplantation (LT) it is very difficult for the clinicians to maintain

normoglycemia. Blood glucose (BG) metabolism suffers from huge disturbance and the patients’

pathological state changes rush. It is almost unaccomplished to monitor all the relevant blood

glucose metabolic functions. The number of physiological parameters we can observe is quite

limited. Thus insulin sensitivity (SI) can be a key-point to observe the changes and to develop patient

and pathological state specific treatments.

There are protocols in the intensive therapies, that estimate the SI values in a model-based way.

STAR (Stochastic Targeted) is also a model-based protocol. On the strength of the estimated SI value

we can predict the possible future SI values based on a stochastic model. If we know the possible

future SI values we are able to plan an adequate patient-state specific therapy.

This study investigates model-based SI value distribution in the LT during and after the surgery.

It analysis the performance of the prediction (using the SI joint probability distribution) of the rush

changes during the liver-transplantation surgery.

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